---
title: "Explore Housing and Electricity Relationship in LA"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```


```{r}
library(tidyverse)
library(ggplot2)
library(scales)
library(viridis)  
library(igraph)
library(plotly)
library(sp)
library(maps)
library(maptools)
library(wordcloud2) 
library(tm)
library(SnowballC)
library(wordcloud)
library(RColorBrewer)
Sys.setenv(MAPBOX_TOKEN = 11122223333444)
```


```{r}
electricity = read.csv("Electricity.csv")

groupbys <- electricity %>%
  select('Owner', 'County', 'Highest_kV') %>%
  group_by(County) %>%
  drop_na()

groupbys['weight'] <- as.numeric(factor(groupbys[['Highest_kV']]))
groupbys <- groupbys %>%
  select('Owner', 'County', 'weight')

groupbys <- groupbys %>%
  filter(County %in% c('Los Angeles', 'San Diego', 
                       'Orange', 'Riverside', 'San Bernardino',
                       'Santa Clara', 'Sacramento', 'San Francisco',
                       'Kern', 'Fresno'))

graphdf <- groupbys %>%
  group_by(Owner, County) %>%
  summarize(total_weight = sum(weight))

graphdf <- graphdf %>% drop_na()

```


### The network relationship between electricity station owner and top 10 CA cities.


```{r}
graphdf1 <- graph_from_data_frame(graphdf, directed = TRUE)

V(graphdf1)[1:10]$colour = 'gray'
V(graphdf1)[10]$colour = 'chocolate'
V(graphdf1)[11]$colour = 'chocolate1'
V(graphdf1)[12]$colour = 'chocolate2'
V(graphdf1)[13]$colour = 'chocolate3'
V(graphdf1)[14]$colour = 'coral'
V(graphdf1)[15]$colour = 'coral1'
V(graphdf1)[16]$colour = 'coral2'
V(graphdf1)[17]$colour = 'coral3'
V(graphdf1)[18]$colour = 'burlywood1'
V(graphdf1)[19]$colour = 'burlywood2'

E(graphdf1)$weight <- 2*scale(graphdf$total_weight) + 3

par(mar = c(0.3, 0.3, 1, 0.3))
plot(graphdf1, 
   layout=layout_in_circle,
   edge.arrow.size = 0.3,
   vertex.size=20, 
   edge.width = E(graphdf1)$weight,
   vertex.color = adjustcolor(V(graphdf1)$colour,alpha.f = .8))

```



```{r}
consumption <- read.csv("Consumption.csv")

```

### Electricity Consumption in LA with Time Series

```{r}
fig <- plot_ly(
  type = "scatter",
  x = as.Date(consumption$County, format = "%Y"),
  y = consumption$LA,
  name = 'Electricity Consumption in LA',
  mode = "markers",
)

fig <- fig %>%
  layout(
    title = "Electricity Consumption in LA"
  )

fig
```

***

```{r}
consumption
```


```{r}
housing = read_csv("housing.csv")

latlong2county <- function(pointsDF) {
    # Prepare SpatialPolygons object with one SpatialPolygon
    # per county
    counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
    IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
    counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                     proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Convert pointsDF to a SpatialPoints object 
    pointsSP <- SpatialPoints(pointsDF[1:2], 
                    proj4string=CRS("+proj=longlat +datum=WGS84"))

    # Use 'over' to get _indices_ of the Polygons object containing each point 
    indices <- over(pointsSP, counties_sp)

    # Return the county names of the Polygons object containing each point
    countyNames <- sapply(counties_sp@polygons, function(x) x@ID)
    countyNames[indices]
}

# Test the function using points in Wisconsin and Oregon.
testPoints <- data.frame(x = c(-90, -120), y = c(44, 44))

housing['county'] = latlong2county(housing)

housing <- housing %>%
  filter(county %in% c("california,los angeles"))

housing$Category[housing$median_house_value<=100000] = '<100K'
housing$Category[housing$median_house_value>100000 & housing$median_house_value<=200000] = '100K~200K'
housing$Category[housing$median_house_value>200000 & housing$median_house_value<=300000] = '200K~300K'
housing$Category[housing$median_house_value>300000 & housing$median_house_value<=400000] = '300K~400K'
housing$Category[housing$median_house_value>400000 ] = '>400K'

```

### Los Angeles Housing Prices Distribution With Population.


```{r}
options(warn=-1)

plot_map = ggplot(housing, 
                  aes(x = longitude, y = latitude, color = median_house_value, 
                      hma = housing_median_age, tr = total_rooms, tb = total_bedrooms,
                      hh = households, mi = median_income)) +
              geom_point(aes(size = population), alpha = 0.6) +
              xlab("Longitude") +
              ylab("Latitude") +
              ggtitle("Los Angeles Housing Price Distribution") +
              theme(plot.title = element_text(hjust = 0.5)) +
              scale_color_viridis(option = "D", labels = comma) + 
              labs(color = "Median House Value (in $USD)", size = "Population")
plot_map
```

### The distribution of housing price amoung ocean proximity in LA

```{r}
fig <- plot_ly(housing, x = ~ocean_proximity, color = ~Category) %>%
  add_histogram()

fig <- fig %>%
  layout(
    title = "The distribution of housing price amoung ocean proximity in LA"
  )

fig
```


### WordCloud of LA House and Electricity Article

```{r}
text <- readLines('LA_housing_overview.txt')

text <- sapply(text,function(row) iconv(row, "latin1", "ASCII", sub=""))

corpus = VCorpus(VectorSource(text))
corpus = tm_map(corpus, content_transformer(tolower))
corpus = tm_map(corpus, removeNumbers)
corpus = tm_map(corpus, removePunctuation)
corpus = tm_map(corpus, removeWords, stopwords())
#corpus = tm_map(corpus, stemDocument)
corpus = tm_map(corpus, stripWhitespace)


```


```{r}
dtm = DocumentTermMatrix(corpus)
dtm = removeSparseTerms(dtm, 0.9999)
dataset = as.matrix(dtm)
v = sort(colSums(dataset),decreasing=TRUE)
myNames = names(v)
d = data.frame(word=myNames,freq=v)
pal2 <- brewer.pal(8,"Dark2")
wordcloud(d$word, colors = pal2,scale=c(4,.6), random.color=FALSE, d$freq, min.freq=15, max.words=200, random.order=FALSE)
```


### ABOUT

*** 
About